Tier 1: Static Recommendations
You run a mobile WAP store with ringtones, full-length songs and wallpapers and want to add a link on each page for “Related Items” to increase user purchases. Or maybe you have an online video store which sells episodes from TV shows and want to provide a link to related shows from each episode.

“Static Recommendations” is a fancy way of saying “If you like A, you might like B.” This technology is often used to help users explore similar content on a service. More technically put, MediaUnbound's Static Recommendations system makes individual item recommendations based on a single input point. The number of recommendations and basic recommendation parameters (adventurous, familiar, etc.) can be set globally by the customer in consultation with MediaUnbound. All data exchanged will be in the customer’s own ID system.

Integration of static recommendations involves our simplest, fastest, and most basic personalization option. Static Recommendations are integrated through Asynchronous Data Transfer. MediaUnbound will provide regular (once every 24 hours, for example) datafeeds of recommendation data for loading into the Client service. Clients provide MediaUnbound with similarly regular datafeeds containing lists of content for which they would like recommendations. Data transfer can take place via ftp site or other mutually agreed protocol. Data can be formatted as xml files, flat text-files or another mutually agreed format. This option is ideal for smaller services, or services with limited ability for user interaction over a long period of time (for example, in-store kiosks).
TransWorld Entertainment has a network of in-store kiosks for burning custom CDs and downloading full-length tracks to digital devices. Each artist, album and track in the catalog displays related artists, albums, and tracks, respectively. The recommendations are custom-created for TWE's specific catalog and tuned for a purchase environment.
MTVN’s URGE music subscription service utilized the Personalization Platform to create auto-mixes (playlists) for their listeners. Listeners could dynamically generate a mix personalized for their musical tastes with a single click, in addition to generating personalized playlists based on a genre, mood or era. Sliders allowed users to interact directly with the Personalization Platform along axes such as new/old, obscure/popular and more/less familiar.